Overview

Dataset statistics

Number of variables15
Number of observations18835
Missing cells0
Missing cells (%)0.0%
Duplicate rows2205
Duplicate rows (%)11.7%
Total size in memory3.3 MiB
Average record size in memory186.4 B

Variable types

Categorical3
Numeric12

Alerts

Dataset has 2205 (11.7%) duplicate rowsDuplicates
song_name has a high cardinality: 13070 distinct values High cardinality
acousticness is highly correlated with energy and 1 other fieldsHigh correlation
energy is highly correlated with acousticness and 1 other fieldsHigh correlation
loudness is highly correlated with acousticness and 2 other fieldsHigh correlation
instrumentalness is highly correlated with loudnessHigh correlation
tempo is highly correlated with time_signatureHigh correlation
time_signature is highly correlated with tempoHigh correlation
song_popularity has 272 (1.4%) zeros Zeros
instrumentalness has 7150 (38.0%) zeros Zeros
key has 2182 (11.6%) zeros Zeros

Reproduction

Analysis started2022-10-16 23:23:31.860395
Analysis finished2022-10-16 23:23:40.226633
Duration8.37 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

song_name
Categorical

HIGH CARDINALITY

Distinct13070
Distinct (%)69.4%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Better
 
21
FEFE (feat. Nicki Minaj & Murda Beatz)
 
19
MIA (feat. Drake)
 
18
Taki Taki (with Selena Gomez, Ozuna & Cardi B)
 
18
No Stylist
 
17
Other values (13065)
18742 

Length

Max length143
Median length92
Mean length16.6173082
Min length1

Characters and Unicode

Total characters312987
Distinct characters308
Distinct categories18 ?
Distinct scripts8 ?
Distinct blocks8 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10174 ?
Unique (%)54.0%

Sample

1st rowBoulevard of Broken Dreams
2nd rowIn The End
3rd rowSeven Nation Army
4th rowBy The Way
5th rowHow You Remind Me

Common Values

ValueCountFrequency (%)
Better21
 
0.1%
FEFE (feat. Nicki Minaj & Murda Beatz)19
 
0.1%
MIA (feat. Drake)18
 
0.1%
Taki Taki (with Selena Gomez, Ozuna & Cardi B)18
 
0.1%
No Stylist17
 
0.1%
Promises (with Sam Smith)16
 
0.1%
Electricity (with Dua Lipa)16
 
0.1%
I Love It (& Lil Pump)16
 
0.1%
Mo Bamba16
 
0.1%
Sunflower - Spider-Man: Into the Spider-Verse16
 
0.1%
Other values (13060)18662
99.1%

Length

2022-10-16T18:23:40.269342image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2458
 
4.1%
the1614
 
2.7%
feat1496
 
2.5%
you1051
 
1.8%
me909
 
1.5%
i735
 
1.2%
love650
 
1.1%
my542
 
0.9%
a532
 
0.9%
to514
 
0.9%
Other values (9918)49458
82.5%

Most occurring characters

ValueCountFrequency (%)
41124
 
13.1%
e29073
 
9.3%
a18940
 
6.1%
o18724
 
6.0%
i16006
 
5.1%
t14461
 
4.6%
n14373
 
4.6%
r12900
 
4.1%
s9650
 
3.1%
l9621
 
3.1%
Other values (298)128115
40.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter199910
63.9%
Uppercase Letter56970
 
18.2%
Space Separator41124
 
13.1%
Other Punctuation5223
 
1.7%
Close Punctuation2427
 
0.8%
Open Punctuation2427
 
0.8%
Decimal Number2424
 
0.8%
Dash Punctuation2116
 
0.7%
Other Letter194
 
0.1%
Currency Symbol62
 
< 0.1%
Other values (8)110
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
ا9
 
4.6%
ج3
 
1.5%
ي3
 
1.5%
3
 
1.5%
ر3
 
1.5%
ل3
 
1.5%
د3
 
1.5%
ز2
 
1.0%
2
 
1.0%
2
 
1.0%
Other values (151)161
83.0%
Lowercase Letter
ValueCountFrequency (%)
e29073
14.5%
a18940
 
9.5%
o18724
 
9.4%
i16006
 
8.0%
t14461
 
7.2%
n14373
 
7.2%
r12900
 
6.5%
s9650
 
4.8%
l9621
 
4.8%
u7114
 
3.6%
Other values (49)49048
24.5%
Uppercase Letter
ValueCountFrequency (%)
S4608
 
8.1%
T4370
 
7.7%
M4297
 
7.5%
L3597
 
6.3%
B3513
 
6.2%
A3112
 
5.5%
R2948
 
5.2%
I2889
 
5.1%
D2790
 
4.9%
C2730
 
4.8%
Other values (25)22116
38.8%
Other Punctuation
ValueCountFrequency (%)
.2122
40.6%
'1507
28.9%
&511
 
9.8%
,506
 
9.7%
?124
 
2.4%
!123
 
2.4%
/113
 
2.2%
"92
 
1.8%
:47
 
0.9%
*41
 
0.8%
Other values (8)37
 
0.7%
Decimal Number
ValueCountFrequency (%)
0555
22.9%
2490
20.2%
1487
20.1%
9248
10.2%
5140
 
5.8%
3118
 
4.9%
4110
 
4.5%
699
 
4.1%
889
 
3.7%
788
 
3.6%
Close Punctuation
ValueCountFrequency (%)
)2373
97.8%
]52
 
2.1%
2
 
0.1%
Open Punctuation
ValueCountFrequency (%)
(2373
97.8%
[52
 
2.1%
2
 
0.1%
Currency Symbol
ValueCountFrequency (%)
$60
96.8%
£1
 
1.6%
¥1
 
1.6%
Math Symbol
ValueCountFrequency (%)
+13
56.5%
|9
39.1%
<1
 
4.3%
Final Punctuation
ValueCountFrequency (%)
54
90.0%
6
 
10.0%
Initial Punctuation
ValueCountFrequency (%)
6
85.7%
1
 
14.3%
Modifier Symbol
ValueCountFrequency (%)
´3
75.0%
`1
 
25.0%
Other Symbol
ValueCountFrequency (%)
®3
75.0%
°1
 
25.0%
Space Separator
ValueCountFrequency (%)
41124
100.0%
Dash Punctuation
ValueCountFrequency (%)
-2116
100.0%
Connector Punctuation
ValueCountFrequency (%)
_9
100.0%
Modifier Letter
ValueCountFrequency (%)
2
100.0%
Format
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin256863
82.1%
Common55913
 
17.9%
Han96
 
< 0.1%
Hangul41
 
< 0.1%
Arabic37
 
< 0.1%
Katakana20
 
< 0.1%
Cyrillic16
 
< 0.1%
Greek1
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
3
 
3.1%
2
 
2.1%
2
 
2.1%
2
 
2.1%
2
 
2.1%
2
 
2.1%
1
 
1.0%
1
 
1.0%
1
 
1.0%
1
 
1.0%
Other values (79)79
82.3%
Latin
ValueCountFrequency (%)
e29073
 
11.3%
a18940
 
7.4%
o18724
 
7.3%
i16006
 
6.2%
t14461
 
5.6%
n14373
 
5.6%
r12900
 
5.0%
s9650
 
3.8%
l9621
 
3.7%
u7114
 
2.8%
Other values (73)106001
41.3%
Common
ValueCountFrequency (%)
41124
73.5%
)2373
 
4.2%
(2373
 
4.2%
.2122
 
3.8%
-2116
 
3.8%
'1507
 
2.7%
0555
 
1.0%
&511
 
0.9%
,506
 
0.9%
2490
 
0.9%
Other values (43)2236
 
4.0%
Hangul
ValueCountFrequency (%)
2
 
4.9%
2
 
4.9%
2
 
4.9%
1
 
2.4%
1
 
2.4%
1
 
2.4%
1
 
2.4%
1
 
2.4%
1
 
2.4%
1
 
2.4%
Other values (28)28
68.3%
Katakana
ValueCountFrequency (%)
2
 
10.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
Other values (9)9
45.0%
Arabic
ValueCountFrequency (%)
ا9
24.3%
ج3
 
8.1%
ي3
 
8.1%
ر3
 
8.1%
ل3
 
8.1%
د3
 
8.1%
ز2
 
5.4%
ع2
 
5.4%
ك2
 
5.4%
و2
 
5.4%
Other values (5)5
13.5%
Cyrillic
ValueCountFrequency (%)
о4
25.0%
е2
12.5%
и2
12.5%
в2
12.5%
н1
 
6.2%
з1
 
6.2%
Р1
 
6.2%
г1
 
6.2%
л1
 
6.2%
к1
 
6.2%
Greek
ValueCountFrequency (%)
ύ1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII312212
99.8%
None490
 
0.2%
CJK96
 
< 0.1%
Punctuation71
 
< 0.1%
Hangul41
 
< 0.1%
Arabic37
 
< 0.1%
Katakana24
 
< 0.1%
Cyrillic16
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
41124
 
13.2%
e29073
 
9.3%
a18940
 
6.1%
o18724
 
6.0%
i16006
 
5.1%
t14461
 
4.6%
n14373
 
4.6%
r12900
 
4.1%
s9650
 
3.1%
l9621
 
3.1%
Other values (78)127340
40.8%
None
ValueCountFrequency (%)
é105
21.4%
ó77
15.7%
í72
14.7%
á63
12.9%
ñ47
9.6%
ú31
 
6.3%
¿12
 
2.4%
ë8
 
1.6%
Ü7
 
1.4%
ç5
 
1.0%
Other values (31)63
12.9%
Punctuation
ValueCountFrequency (%)
54
76.1%
6
 
8.5%
6
 
8.5%
3
 
4.2%
1
 
1.4%
1
 
1.4%
Arabic
ValueCountFrequency (%)
ا9
24.3%
ج3
 
8.1%
ي3
 
8.1%
ر3
 
8.1%
ل3
 
8.1%
د3
 
8.1%
ز2
 
5.4%
ع2
 
5.4%
ك2
 
5.4%
و2
 
5.4%
Other values (5)5
13.5%
Cyrillic
ValueCountFrequency (%)
о4
25.0%
е2
12.5%
и2
12.5%
в2
12.5%
н1
 
6.2%
з1
 
6.2%
Р1
 
6.2%
г1
 
6.2%
л1
 
6.2%
к1
 
6.2%
CJK
ValueCountFrequency (%)
3
 
3.1%
2
 
2.1%
2
 
2.1%
2
 
2.1%
2
 
2.1%
2
 
2.1%
1
 
1.0%
1
 
1.0%
1
 
1.0%
1
 
1.0%
Other values (79)79
82.3%
Katakana
ValueCountFrequency (%)
2
 
8.3%
2
 
8.3%
2
 
8.3%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
Other values (11)11
45.8%
Hangul
ValueCountFrequency (%)
2
 
4.9%
2
 
4.9%
2
 
4.9%
1
 
2.4%
1
 
2.4%
1
 
2.4%
1
 
2.4%
1
 
2.4%
1
 
2.4%
1
 
2.4%
Other values (28)28
68.3%

song_popularity
Real number (ℝ≥0)

ZEROS

Distinct101
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.99187683
Minimum0
Maximum100
Zeros272
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size147.3 KiB
2022-10-16T18:23:40.323047image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q140
median56
Q369
95-th percentile85
Maximum100
Range100
Interquartile range (IQR)29

Descriptive statistics

Standard deviation21.90565432
Coefficient of variation (CV)0.4133775898
Kurtosis-0.1691037112
Mean52.99187683
Median Absolute Deviation (MAD)14
Skewness-0.5014874681
Sum998102
Variance479.8576913
MonotonicityNot monotonic
2022-10-16T18:23:40.375080image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58404
 
2.1%
52389
 
2.1%
55388
 
2.1%
60383
 
2.0%
63378
 
2.0%
62370
 
2.0%
65364
 
1.9%
64364
 
1.9%
61360
 
1.9%
69359
 
1.9%
Other values (91)15076
80.0%
ValueCountFrequency (%)
0272
1.4%
1111
0.6%
2103
 
0.5%
372
 
0.4%
491
 
0.5%
583
 
0.4%
670
 
0.4%
778
 
0.4%
885
 
0.5%
961
 
0.3%
ValueCountFrequency (%)
10012
 
0.1%
9916
 
0.1%
9847
0.2%
9736
 
0.2%
9653
0.3%
9561
0.3%
9485
0.5%
9332
 
0.2%
9262
0.3%
9195
0.5%

song_duration_ms
Real number (ℝ≥0)

Distinct11771
Distinct (%)62.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean218211.5876
Minimum12000
Maximum1799346
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size147.3 KiB
2022-10-16T18:23:40.514677image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum12000
5-th percentile141586
Q1184339.5
median211306
Q3242844
95-th percentile313786
Maximum1799346
Range1787346
Interquartile range (IQR)58504.5

Descriptive statistics

Standard deviation59887.54057
Coefficient of variation (CV)0.2744471145
Kurtosis46.70894882
Mean218211.5876
Median Absolute Deviation (MAD)28813
Skewness3.257477427
Sum4110015252
Variance3586517515
MonotonicityNot monotonic
2022-10-16T18:23:40.559811image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16500025
 
0.1%
18000021
 
0.1%
21250020
 
0.1%
18900020
 
0.1%
17940419
 
0.1%
21036718
 
0.1%
15200018
 
0.1%
19217217
 
0.1%
19500017
 
0.1%
21330916
 
0.1%
Other values (11761)18644
99.0%
ValueCountFrequency (%)
120001
< 0.1%
261861
< 0.1%
313731
< 0.1%
359201
< 0.1%
500141
< 0.1%
505081
< 0.1%
505731
< 0.1%
530661
< 0.1%
545391
< 0.1%
552131
< 0.1%
ValueCountFrequency (%)
17993461
< 0.1%
13559381
< 0.1%
12336661
< 0.1%
10479331
< 0.1%
8668891
< 0.1%
8366661
< 0.1%
8334931
< 0.1%
8295861
< 0.1%
8057461
< 0.1%
7609731
< 0.1%

acousticness
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3209
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2585390186
Minimum1.02 × 10-6
Maximum0.996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size147.3 KiB
2022-10-16T18:23:40.608162image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1.02 × 10-6
5-th percentile0.000633
Q10.0241
median0.132
Q30.424
95-th percentile0.882
Maximum0.996
Range0.99599898
Interquartile range (IQR)0.3999

Descriptive statistics

Standard deviation0.288718909
Coefficient of variation (CV)1.11673244
Kurtosis-0.09627573825
Mean0.2585390186
Median Absolute Deviation (MAD)0.126
Skewness1.071164167
Sum4869.582415
Variance0.08335860841
MonotonicityNot monotonic
2022-10-16T18:23:40.653003image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1345
 
0.2%
0.13544
 
0.2%
0.15343
 
0.2%
0.10241
 
0.2%
0.10740
 
0.2%
0.021539
 
0.2%
0.16138
 
0.2%
0.14138
 
0.2%
0.12837
 
0.2%
0.17336
 
0.2%
Other values (3199)18434
97.9%
ValueCountFrequency (%)
1.02 × 10-61
< 0.1%
1.36 × 10-61
< 0.1%
1.37 × 10-61
< 0.1%
1.4 × 10-61
< 0.1%
1.8 × 10-61
< 0.1%
1.95 × 10-61
< 0.1%
2.01 × 10-61
< 0.1%
2.18 × 10-61
< 0.1%
2.42 × 10-61
< 0.1%
2.58 × 10-61
< 0.1%
ValueCountFrequency (%)
0.99615
0.1%
0.99526
0.1%
0.99420
0.1%
0.99322
0.1%
0.99214
0.1%
0.99117
0.1%
0.9913
0.1%
0.9899
 
< 0.1%
0.98810
 
0.1%
0.9877
 
< 0.1%

danceability
Real number (ℝ≥0)

Distinct849
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6333480701
Minimum0
Maximum0.987
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size147.3 KiB
2022-10-16T18:23:40.698799image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.354
Q10.533
median0.645
Q30.748
95-th percentile0.876
Maximum0.987
Range0.987
Interquartile range (IQR)0.215

Descriptive statistics

Standard deviation0.156722705
Coefficient of variation (CV)0.2474511448
Kurtosis-0.07479676201
Mean0.6333480701
Median Absolute Deviation (MAD)0.107
Skewness-0.3917191172
Sum11929.1109
Variance0.02456200626
MonotonicityNot monotonic
2022-10-16T18:23:40.744085image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.68773
 
0.4%
0.69473
 
0.4%
0.665
 
0.3%
0.65765
 
0.3%
0.75764
 
0.3%
0.61164
 
0.3%
0.69962
 
0.3%
0.64662
 
0.3%
0.65961
 
0.3%
0.7159
 
0.3%
Other values (839)18187
96.6%
ValueCountFrequency (%)
02
< 0.1%
0.05941
< 0.1%
0.06171
< 0.1%
0.06251
< 0.1%
0.0661
< 0.1%
0.06741
< 0.1%
0.06841
< 0.1%
0.07221
< 0.1%
0.0811
< 0.1%
0.08121
< 0.1%
ValueCountFrequency (%)
0.9871
 
< 0.1%
0.9811
 
< 0.1%
0.981
 
< 0.1%
0.9783
< 0.1%
0.9753
< 0.1%
0.9721
 
< 0.1%
0.9711
 
< 0.1%
0.971
 
< 0.1%
0.9691
 
< 0.1%
0.9682
< 0.1%

energy
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1132
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6449947566
Minimum0.00107
Maximum0.999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size147.3 KiB
2022-10-16T18:23:40.790120image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.00107
5-th percentile0.237
Q10.51
median0.674
Q30.815
95-th percentile0.941
Maximum0.999
Range0.99793
Interquartile range (IQR)0.305

Descriptive statistics

Standard deviation0.2141007564
Coefficient of variation (CV)0.3319418557
Kurtosis-0.1378745539
Mean0.6449947566
Median Absolute Deviation (MAD)0.151
Skewness-0.620737507
Sum12148.47624
Variance0.0458391339
MonotonicityNot monotonic
2022-10-16T18:23:40.837146image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.70462
 
0.3%
0.6358
 
0.3%
0.71757
 
0.3%
0.7353
 
0.3%
0.78652
 
0.3%
0.80552
 
0.3%
0.84951
 
0.3%
0.69451
 
0.3%
0.78350
 
0.3%
0.78549
 
0.3%
Other values (1122)18300
97.2%
ValueCountFrequency (%)
0.001072
< 0.1%
0.001631
< 0.1%
0.002051
< 0.1%
0.002121
< 0.1%
0.002641
< 0.1%
0.002661
< 0.1%
0.002891
< 0.1%
0.003051
< 0.1%
0.003441
< 0.1%
0.003621
< 0.1%
ValueCountFrequency (%)
0.9991
 
< 0.1%
0.9973
 
< 0.1%
0.9966
 
< 0.1%
0.9958
< 0.1%
0.99410
0.1%
0.9937
 
< 0.1%
0.9928
< 0.1%
0.99113
0.1%
0.9919
0.1%
0.98914
0.1%

instrumentalness
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct3925
Distinct (%)20.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.07800803885
Minimum0
Maximum0.997
Zeros7150
Zeros (%)38.0%
Negative0
Negative (%)0.0%
Memory size147.3 KiB
2022-10-16T18:23:40.884475image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.14 × 10-5
Q30.00257
95-th percentile0.782
Maximum0.997
Range0.997
Interquartile range (IQR)0.00257

Descriptive statistics

Standard deviation0.2215906093
Coefficient of variation (CV)2.840612488
Kurtosis7.563664367
Mean0.07800803885
Median Absolute Deviation (MAD)1.14 × 10-5
Skewness2.985176362
Sum1469.281412
Variance0.04910239814
MonotonicityNot monotonic
2022-10-16T18:23:40.931107image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07150
38.0%
3.33 × 10-637
 
0.2%
0.0010721
 
0.1%
0.0011420
 
0.1%
0.00051218
 
0.1%
0.0010317
 
0.1%
1.16 × 10-617
 
0.1%
4.91 × 10-616
 
0.1%
1.07 × 10-516
 
0.1%
1.03 × 10-516
 
0.1%
Other values (3915)11507
61.1%
ValueCountFrequency (%)
07150
38.0%
1 × 10-62
 
< 0.1%
1.01 × 10-66
 
< 0.1%
1.02 × 10-68
 
< 0.1%
1.03 × 10-68
 
< 0.1%
1.04 × 10-612
 
0.1%
1.05 × 10-67
 
< 0.1%
1.06 × 10-65
 
< 0.1%
1.07 × 10-69
 
< 0.1%
1.08 × 10-65
 
< 0.1%
ValueCountFrequency (%)
0.9971
 
< 0.1%
0.9891
 
< 0.1%
0.9821
 
< 0.1%
0.9791
 
< 0.1%
0.9781
 
< 0.1%
0.9771
 
< 0.1%
0.9751
 
< 0.1%
0.9741
 
< 0.1%
0.9733
< 0.1%
0.9722
< 0.1%

key
Real number (ℝ≥0)

ZEROS

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.289195646
Minimum0
Maximum11
Zeros2182
Zeros (%)11.6%
Negative0
Negative (%)0.0%
Memory size147.3 KiB
2022-10-16T18:23:40.971890image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.614594613
Coefficient of variation (CV)0.6833921176
Kurtosis-1.311465857
Mean5.289195646
Median Absolute Deviation (MAD)3
Skewness-0.00252001974
Sum99622
Variance13.06529422
MonotonicityNot monotonic
2022-10-16T18:23:41.004847image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
02182
11.6%
12164
11.5%
72032
10.8%
21715
9.1%
91698
9.0%
111600
8.5%
51574
8.4%
61351
7.2%
81349
7.2%
101331
7.1%
Other values (2)1839
9.8%
ValueCountFrequency (%)
02182
11.6%
12164
11.5%
21715
9.1%
3512
 
2.7%
41327
7.0%
51574
8.4%
61351
7.2%
72032
10.8%
81349
7.2%
91698
9.0%
ValueCountFrequency (%)
111600
8.5%
101331
7.1%
91698
9.0%
81349
7.2%
72032
10.8%
61351
7.2%
51574
8.4%
41327
7.0%
3512
 
2.7%
21715
9.1%

liveness
Real number (ℝ≥0)

Distinct1425
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1796503053
Minimum0.0109
Maximum0.986
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size147.3 KiB
2022-10-16T18:23:41.044756image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.0109
5-th percentile0.0575
Q10.0929
median0.122
Q30.221
95-th percentile0.466
Maximum0.986
Range0.9751
Interquartile range (IQR)0.1281

Descriptive statistics

Standard deviation0.1439841652
Coefficient of variation (CV)0.8014690819
Kurtosis5.789919007
Mean0.1796503053
Median Absolute Deviation (MAD)0.042
Skewness2.215422652
Sum3383.7135
Variance0.02073143984
MonotonicityNot monotonic
2022-10-16T18:23:41.094177image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.108219
 
1.2%
0.111206
 
1.1%
0.104197
 
1.0%
0.106195
 
1.0%
0.109190
 
1.0%
0.11188
 
1.0%
0.102187
 
1.0%
0.112187
 
1.0%
0.107186
 
1.0%
0.101178
 
0.9%
Other values (1415)16902
89.7%
ValueCountFrequency (%)
0.01091
 
< 0.1%
0.01191
 
< 0.1%
0.01481
 
< 0.1%
0.01571
 
< 0.1%
0.01861
 
< 0.1%
0.01931
 
< 0.1%
0.01963
< 0.1%
0.02061
 
< 0.1%
0.02151
 
< 0.1%
0.02191
 
< 0.1%
ValueCountFrequency (%)
0.9861
< 0.1%
0.9841
< 0.1%
0.9831
< 0.1%
0.9812
< 0.1%
0.9791
< 0.1%
0.9782
< 0.1%
0.9771
< 0.1%
0.9751
< 0.1%
0.9741
< 0.1%
0.9671
< 0.1%

loudness
Real number (ℝ)

HIGH CORRELATION

Distinct8416
Distinct (%)44.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-7.447434669
Minimum-38.768
Maximum1.585
Zeros0
Zeros (%)0.0%
Negative18828
Negative (%)> 99.9%
Memory size147.3 KiB
2022-10-16T18:23:41.141344image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-38.768
5-th percentile-14.295
Q1-9.044
median-6.555
Q3-4.908
95-th percentile-3.149
Maximum1.585
Range40.353
Interquartile range (IQR)4.136

Descriptive statistics

Standard deviation3.827831185
Coefficient of variation (CV)-0.5139798272
Kurtosis6.522479594
Mean-7.447434669
Median Absolute Deviation (MAD)1.923
Skewness-1.929510614
Sum-140272.432
Variance14.65229158
MonotonicityNot monotonic
2022-10-16T18:23:41.186072image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-4.58924
 
0.1%
-4.20620
 
0.1%
-9.12719
 
0.1%
-6.3518
 
0.1%
-5.14418
 
0.1%
-6.43918
 
0.1%
-8.30417
 
0.1%
-5.99117
 
0.1%
-5.59317
 
0.1%
-5.35117
 
0.1%
Other values (8406)18650
99.0%
ValueCountFrequency (%)
-38.7681
< 0.1%
-36.7291
< 0.1%
-36.2811
< 0.1%
-35.4491
< 0.1%
-35.3892
< 0.1%
-34.2551
< 0.1%
-33.9291
< 0.1%
-33.8591
< 0.1%
-33.4931
< 0.1%
-33.2461
< 0.1%
ValueCountFrequency (%)
1.5851
< 0.1%
1.3421
< 0.1%
0.8781
< 0.1%
0.5251
< 0.1%
0.1981
< 0.1%
0.1191
< 0.1%
0.0521
< 0.1%
-0.2571
< 0.1%
-0.3981
< 0.1%
-0.5781
< 0.1%

audio_mode
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
1
11831 
0
7004 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
111831
62.8%
07004
37.2%

Length

2022-10-16T18:23:41.229146image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-16T18:23:41.272296image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
111831
62.8%
07004
37.2%

Most occurring characters

ValueCountFrequency (%)
111831
62.8%
07004
37.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number18835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
111831
62.8%
07004
37.2%

Most occurring scripts

ValueCountFrequency (%)
Common18835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
111831
62.8%
07004
37.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII18835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
111831
62.8%
07004
37.2%

speechiness
Real number (ℝ≥0)

Distinct1224
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.102099124
Minimum0
Maximum0.941
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size147.3 KiB
2022-10-16T18:23:41.315290image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0288
Q10.0378
median0.0555
Q30.119
95-th percentile0.334
Maximum0.941
Range0.941
Interquartile range (IQR)0.0812

Descriptive statistics

Standard deviation0.1043784804
Coefficient of variation (CV)1.022324936
Kurtosis6.504976944
Mean0.102099124
Median Absolute Deviation (MAD)0.0227
Skewness2.271017971
Sum1923.037
Variance0.01089486717
MonotonicityNot monotonic
2022-10-16T18:23:41.361117image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.033766
 
0.4%
0.03261
 
0.3%
0.03161
 
0.3%
0.036260
 
0.3%
0.03859
 
0.3%
0.031858
 
0.3%
0.032958
 
0.3%
0.043956
 
0.3%
0.045755
 
0.3%
0.033355
 
0.3%
Other values (1214)18246
96.9%
ValueCountFrequency (%)
02
< 0.1%
0.02241
 
< 0.1%
0.02284
< 0.1%
0.02291
 
< 0.1%
0.02313
< 0.1%
0.02332
< 0.1%
0.02342
< 0.1%
0.02352
< 0.1%
0.02364
< 0.1%
0.02382
< 0.1%
ValueCountFrequency (%)
0.9411
< 0.1%
0.941
< 0.1%
0.9361
< 0.1%
0.9151
< 0.1%
0.9061
< 0.1%
0.8941
< 0.1%
0.8912
< 0.1%
0.891
< 0.1%
0.8692
< 0.1%
0.8312
< 0.1%

tempo
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12112
Distinct (%)64.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.0731543
Minimum0
Maximum242.318
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size147.3 KiB
2022-10-16T18:23:41.404920image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile79.4165
Q198.368
median120.013
Q3139.931
95-th percentile174.1473
Maximum242.318
Range242.318
Interquartile range (IQR)41.563

Descriptive statistics

Standard deviation28.71445573
Coefficient of variation (CV)0.2371661653
Kurtosis-0.2175166945
Mean121.0731543
Median Absolute Deviation (MAD)20.049
Skewness0.4428545765
Sum2280412.861
Variance824.5199676
MonotonicityNot monotonic
2022-10-16T18:23:41.537303image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
125.97820
 
0.1%
95.94819
 
0.1%
120.01318
 
0.1%
97.06418
 
0.1%
123.0717
 
0.1%
147.05517
 
0.1%
89.93116
 
0.1%
128.03816
 
0.1%
104.05316
 
0.1%
118.15916
 
0.1%
Other values (12102)18662
99.1%
ValueCountFrequency (%)
02
< 0.1%
46.5911
< 0.1%
47.9531
< 0.1%
51.6071
< 0.1%
52.1811
< 0.1%
54.2131
< 0.1%
56.9831
< 0.1%
56.9851
< 0.1%
571
< 0.1%
57.1071
< 0.1%
ValueCountFrequency (%)
242.3181
< 0.1%
216.1151
< 0.1%
214.6861
< 0.1%
213.991
< 0.1%
213.2261
< 0.1%
212.1371
< 0.1%
212.0581
< 0.1%
211.6441
< 0.1%
211.3571
< 0.1%
210.751
< 0.1%

time_signature
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
4
17754 
3
 
772
5
 
233
1
 
73
0
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18835
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
417754
94.3%
3772
 
4.1%
5233
 
1.2%
173
 
0.4%
03
 
< 0.1%

Length

2022-10-16T18:23:41.579645image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-16T18:23:41.619011image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
417754
94.3%
3772
 
4.1%
5233
 
1.2%
173
 
0.4%
03
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
417754
94.3%
3772
 
4.1%
5233
 
1.2%
173
 
0.4%
03
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number18835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
417754
94.3%
3772
 
4.1%
5233
 
1.2%
173
 
0.4%
03
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common18835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
417754
94.3%
3772
 
4.1%
5233
 
1.2%
173
 
0.4%
03
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII18835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
417754
94.3%
3772
 
4.1%
5233
 
1.2%
173
 
0.4%
03
 
< 0.1%

audio_valence
Real number (ℝ≥0)

Distinct1246
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5279668755
Minimum0
Maximum0.984
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size147.3 KiB
2022-10-16T18:23:41.660862image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.132
Q10.335
median0.527
Q30.725
95-th percentile0.924
Maximum0.984
Range0.984
Interquartile range (IQR)0.39

Descriptive statistics

Standard deviation0.2446316877
Coefficient of variation (CV)0.4633466588
Kurtosis-0.9776704833
Mean0.5279668755
Median Absolute Deviation (MAD)0.195
Skewness-0.01642321694
Sum9944.2561
Variance0.05984466264
MonotonicityNot monotonic
2022-10-16T18:23:41.715640image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.96171
 
0.4%
0.96461
 
0.3%
0.37653
 
0.3%
0.50553
 
0.3%
0.59152
 
0.3%
0.96251
 
0.3%
0.32948
 
0.3%
0.49346
 
0.2%
0.42242
 
0.2%
0.31942
 
0.2%
Other values (1236)18316
97.2%
ValueCountFrequency (%)
02
< 0.1%
0.0231
< 0.1%
0.02431
< 0.1%
0.02771
< 0.1%
0.02922
< 0.1%
0.03011
< 0.1%
0.03091
< 0.1%
0.03121
< 0.1%
0.03161
< 0.1%
0.0322
< 0.1%
ValueCountFrequency (%)
0.9841
 
< 0.1%
0.9823
< 0.1%
0.9812
 
< 0.1%
0.983
< 0.1%
0.9792
 
< 0.1%
0.9785
< 0.1%
0.9774
< 0.1%
0.9765
< 0.1%
0.9756
< 0.1%
0.9746
< 0.1%

Interactions

2022-10-16T18:23:39.490101image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:33.522320image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:34.130890image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:34.748070image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:35.252266image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:35.746989image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:36.326761image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:36.822827image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:37.308469image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:37.882681image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:38.403587image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:38.976611image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:39.532517image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:33.606492image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:34.174044image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:34.790089image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:35.292668image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:35.786055image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:36.366264image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:36.862724image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:37.348293image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:37.924777image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:38.442535image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-16T18:23:39.577746image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:33.676963image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:34.220103image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:34.835103image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-16T18:23:39.062602image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:39.619780image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:33.735149image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:34.263547image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:34.876014image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:35.376786image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:35.869698image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:36.452527image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:36.947257image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:37.517654image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:38.014993image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:38.525800image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:39.103842image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:39.661786image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:33.794186image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:34.306823image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:34.917564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:35.418587image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:35.910876image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:36.492963image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:36.987893image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:37.557329image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:38.057332image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:38.566772image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:39.144640image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:39.703130image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:33.836948image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:34.350671image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:34.959047image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:35.459903image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:36.038533image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:36.534083image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:37.028100image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:37.596975image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:38.101140image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:38.608197image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:39.185391image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:39.745376image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:33.880453image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:34.395226image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:35.001114image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:35.499976image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:36.078403image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:36.576214image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:37.068020image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:37.637103image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:38.143480image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:38.649130image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:39.225893image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:39.786304image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:33.921399image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:34.528731image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:35.042333image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:35.539768image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:36.117994image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-16T18:23:33.963574image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:34.571555image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:35.084180image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-16T18:23:36.159637image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:36.656995image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:37.146858image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:37.716733image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-16T18:23:38.728988image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:39.307436image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:39.871313image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:34.008220image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:34.617617image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:35.128608image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:35.624717image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-16T18:23:39.352131image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:39.910978image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:34.047556image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:34.660501image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:35.168939image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:35.664256image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:36.245602image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:36.740304image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:37.229055image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:37.800705image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:38.317644image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:38.809265image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:39.392446image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:39.951616image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:34.089295image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:34.703899image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:35.210132image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:35.706275image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:36.286205image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:36.781331image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:37.268538image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:37.841647image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:38.360677image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:38.848960image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:23:39.448688image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-10-16T18:23:41.767181image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-16T18:23:41.843375image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-16T18:23:41.918574image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-16T18:23:41.985161image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-10-16T18:23:42.033526image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-16T18:23:40.028535image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-16T18:23:40.155738image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

song_namesong_popularitysong_duration_msacousticnessdanceabilityenergyinstrumentalnesskeylivenessloudnessaudio_modespeechinesstempotime_signatureaudio_valence
0Boulevard of Broken Dreams732623330.0055200.4960.6820.00002980.0589-4.09510.0294167.06040.474
1In The End662169330.0103000.5420.8530.00000030.1080-6.40700.0498105.25640.370
2Seven Nation Army762317330.0081700.7370.4630.44700000.2550-7.82810.0792123.88140.324
3By The Way742169330.0264000.4510.9700.00355000.1020-4.93810.1070122.44440.198
4How You Remind Me562238260.0009540.4470.7660.000000100.1130-5.06510.0313172.01140.574
5Bring Me To Life802358930.0089500.3160.9450.00000240.3960-3.16900.1240189.93140.320
6Last Resort811998930.0005040.5810.8870.00111040.2680-3.65900.062490.57840.724
7Are You Gonna Be My Girl762138000.0014800.6130.9530.00058220.1520-3.43510.0855105.04640.537
8Mr. Brightside802225860.0010800.3300.9360.00000010.0926-3.66010.0917148.11240.234
9Sex on Fire812033460.0017200.5420.9050.01040090.1360-5.65310.0540153.39840.374

Last rows

song_namesong_popularitysong_duration_msacousticnessdanceabilityenergyinstrumentalnesskeylivenessloudnessaudio_modespeechinesstempotime_signatureaudio_valence
18825Something Familiar601454660.9060.4910.04090.00001500.0896-18.43110.0383131.05330.2780
18826Call It Dreaming672317600.6100.5190.51500.00005750.1070-9.44810.031080.32940.7140
18827Stay Awake551145820.8980.3700.13600.00026370.0999-13.52810.0433146.08140.0592
18828Build Me Up From Bones642161730.8620.5150.28600.00006950.1060-11.77610.0378115.07640.2840
18829I Know621951060.3950.6440.52300.00000040.0930-7.66010.037895.96640.4450
18830Let It Breathe601596450.8930.5000.15100.000065110.1110-16.10710.0348113.96940.3000
18831Answers602056660.7650.4950.16100.000001110.1050-14.07800.030194.28640.2650
18832Sudden Love (Acoustic)231822110.8470.7190.32500.00000000.1250-12.22210.0355130.53440.2860
18833Gentle on My Mind553522800.9450.4880.32600.01570030.1190-12.02010.0328106.06340.3230
18834Up to Me601935330.9110.6400.38100.00025440.1040-11.79010.030291.49040.5810

Duplicate rows

Most frequently occurring

song_namesong_popularitysong_duration_msacousticnessdanceabilityenergyinstrumentalnesskeylivenessloudnessaudio_modespeechinesstempotime_signatureaudio_valence# duplicates
586FEFE (feat. Nicki Minaj & Murda Beatz)961794040.088000.9310.3870.00000010.1360-9.12710.4120125.97840.37619
1167MIA (feat. Drake)942103670.014300.8180.5400.00051260.0990-6.35000.054497.06440.17418
1811Taki Taki (with Selena Gomez, Ozuna & Cardi B)982125000.153000.8410.7980.00000310.0618-4.20600.229095.94840.59118
1362No Stylist911921720.021500.7650.7040.00000050.2270-4.58900.1270147.05540.49817
561Electricity (with Dua Lipa)942381730.010400.5880.6700.00000300.3380-6.43910.0473118.15940.50516
864I Love It (& Lil Pump)991279460.011400.9010.5220.00000020.2590-8.30410.3300104.05340.32916
1481Promises (with Sam Smith)982133090.011900.7810.7680.000005110.3250-5.99110.0394123.07040.48616
1773Sunflower - Spider-Man: Into the Spider-Verse661580530.541000.7570.5010.00000020.0718-5.59310.046689.93140.91016
196Be Alright961963730.697000.5530.5860.000000110.0813-6.31910.0362126.68440.44314
2038Wake Up in the Sky922046640.003810.8000.5780.00000040.3590-5.14400.0485143.01040.36714